FLAN-T5-XXL a Potential Terminator of Hospitality Central Reservation System (CRS) Reporting Function?

Remember T-1000? The primary antagonist in Terminator 2, a highly advanced deadly assassin Robert, is sent by Skynet the Artificial Intelligence System to kill John Connor the future leader of the human resistance. Made of liquid metal, referred to as "mimetic poly-alloy" capable of morphing its shape, imitating other humans, and recovering quickly from damage. 

Figure 1: T1000 from Terminator 2

Well, we have to get there, or maybe hope, we will not be there! Let’s first understand what is FLAN-T5-XXL.

What is T5?

T5 a pre-trained Text-to-Text Transfer Transformer (encoder-decoder) Large Language Model (LLM) based on Transformer architecture, designed to handle diverse Natural Language Processing (NLP) tasks. T5’s unique ability is that it can formulate all NLP tasks i.e. classification, summarization, translation, or question answering as text-to-text problems, making it versatile across domains.

What is FLAN-T5?

A variant of T5, fine-tuned with FLAN (Fine-tuned Language Models on Annotated Natural Language Tasks) technique,  based on the same encoder-decoder architecture (Figure 2) as T5 but introduces additional fine-tuning on the instruction-based dataset. Hence effective at instructions following tasks, making it a suitable candidate to develop an explicit instruction understanding and answering system on top of it.

What is FLAN-T5-XXL?

FLAN-T5 has variants ranging from Small (60M parameters) to XXL (11B parameters). The models increase in complexity and resource demands, with XXL excelling in multi-step reasoning and long-form text generation, while smaller models prioritize efficiency for lightweight tasks. Larger models provide better accuracy and generalization. 

Figure 2: T5 Transformer Architecture

 What is a CRS in the hospitality context?

CRS, stands for Central Reservation System is an IT system used by hotels, resorts, and other accommodation providers to efficiently manage room inventory, pricing, and reservations. It ensures efficient booking management by aggregating data from various channels.  The key functions of CRS include (Figure 3): 

Figure 3: Key functions of a CRS

Central Booking Management: help manage all reservations in a single platform irrespective of the source of booking direct, third party, OTA (online travel agencies), or GDS (global distribution system).

Inventory Management: Ensures consistent room availability and pricing data across the booking channels

Rate Management: Facilitates dynamic upgradation of price plan and rate plan across all platforms

Channel management: facilitates integration with channel manager helping the distribution of inventory across multiple sales platforms example GDS, OTAs, and meta-search engines like Google Hotel Ads or TripAdvisor.

Reporting: Helps hotel operators make informed and strategic decisions by providing analytical and operational reports on booking, inventory, revenue, occupancy trends, price plans, rate plans, etc.

Many times, low on priority compared to other components, the Reporting function of CRS, is an essential utility for operation optimization, enhancing guest experience, and driving revenue.

Current State of Reporting function and impact of LLM on it

Travel tech industries offering CRS software make a significant investment in building data and analytical platforms to facilitate the reporting function of CRS or maybe any other hospitality products (GMS, PMS, etc.) for that matter. Many times, these are pre-defined KPIs or insights in the form of pre-developed bundled analytical or operational reports, or maybe APIs offering information required to facilitate hotel operations. The CRS (in general hospitality) software makers either choose the traditional application tech stack (e.g. Java full stack with angular etc.) or analytical tools (Qlik, Power BI, etc.) to develop the insights. Any additional KPIs / Report demand from the hotel operator goes through the tedious, time-consuming software development life cycle causing frustration among the hoteliers. The Advancement of GenAI especially the LLM like FLAN-T5-XXL having the ability to capture more intricate language patterns and contextual relationships can generate insights based on prompt (or command) on demand. This technological advancement will be a game changer transferring the responsibility of extracting insights (importantly what insights) from the tech provider to the hoteliers, letting the travel tech companies focus on building a data repository and periodical fine-tuning of the FLAN-T5-XXL. A typical deployment is demonstrated in Figure 4.

Figure 4: Deployment of a FLAN-T5-XXL as Reporting function

Conclusion

So, is the LLM like FLAN-T5-XXL going to kill the reporting function of CRS, or any other hospitality product, or maybe any ERP product for that matter? Well, it depends, if the reporting function of the product is purely operational in nature fetching only transactional insight or maybe a summary of it, yes sooner the hotelier will ask it to replace it with a GenAI model. But if the reporting function is rich in data visualization, slicing dicing, drill down, and what-if analysis instead of killing the reporting function LLM (FLAN-T5-XXL) is more likely to augment and enhance it, making it more accessible, intelligent, and user-friendly!

Either way, LLM adoption is inevitable for reporting functions. Hasta la vista!